J. Lange

Narrowing RIFT: Focused simulation-based-inference for interpreting exceptional GW sources

K. J. Wagner, R. O’Shaughnessy, A. Yelikar, N. Manning et autres

The Rapid Iterative FiTting (RIFT) parameter inference algorithm provides a simulation-based inference approach to efficient, highly-parallelized parameter inference for GW sources. Previous editions of RIFT have conservatively optimized for robust inference about poorly constrained observations. In this paper, we summarize algorithm enhancements …

Ultralight vector dark matter search using data from the KAGRA O3GK run

The LIGO Scientific Collaboration, the KAGRA Collaboration, Adrian Abac, R. Abbott et autres

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing …

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

The LIGO Scientific Collaboration, the KAGRA Collaboration, R. Abbott, H. Abe et autres

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all …

Expanding RIFT: Improving performance for GW parameter inference

J. Wofford, A. Yelikar, H. Gallagher, E. Champion et autres

The Rapid Iterative FiTting (RIFT) parameter inference algorithm provides a framework for efficient, highly-parallelized parameter inference for GW sources. In this paper, we summarize essential algorithm enhancements and operating point choices for the RIFT iterative algorithm, including choices used for analysis of …

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